plotAnimatedFlowmap: Generating flow maps and animations based on transition probabilities

Description

This is the main function for generating flow maps and animations based on transition probabilities including self states and excluding self states. Flow maps are a type of data visualization used to represent the transition probability of different states. Animations are the gifs used to represent the movement of data through the cells.

Usage

plotAnimatedFlowmap(
  hvt_model_output,
  transition_probability_df,
  df,
  flow_map = "All",
  cellid_column,
  time_column
)

Value

A list of flow maps and animation gifs.

Arguments

hvt_model_output: List. Output from a trainHVT function.
transition_probability_df: List. Output from getTransitionProbability function
df: Data frame. The input dataframe should contain two columns, cell ID from scoreHVT function and time stamp of that dataset.
flow_map: Character. Type of flow map ('self_state', 'without_self_state', 'All' or NULL)
cellid_column: Character. Name of the column containing cell IDs.
time_column: Character. Name of the column containing time stamps

Author

PonAnuReka Seenivasan <ponanureka.s@mu-sigma.com>, Vishwavani <vishwavani@mu-sigma.com>

Examples

Run this code

dataset <- data.frame(date = as.numeric(time(EuStockMarkets)),
                      DAX = EuStockMarkets[, "DAX"],
                      SMI = EuStockMarkets[, "SMI"],
                      CAC = EuStockMarkets[, "CAC"],
                      FTSE = EuStockMarkets[, "FTSE"])
hvt.results<- trainHVT(dataset,n_cells = 60, depth = 1, quant.err = 0.1,
                       distance_metric = "L1_Norm", error_metric = "max",
                       normalize = TRUE,quant_method = "kmeans")
scoring <- scoreHVT(dataset, hvt.results)
cell_id <- scoring$scoredPredictedData$Cell.ID
time_stamp <- dataset$date
dataset <- data.frame(cell_id, time_stamp)
table <- getTransitionProbability(dataset, cellid_column = "cell_id",
time_column = "time_stamp")
plots <- plotAnimatedFlowmap(hvt_model_output = hvt.results,
transition_probability_df = table,df = dataset,
flow_map = 'All',cellid_column = "cell_id", time_column = "time_stamp")